import cv2
from matplotlib.pyplot import gray
import numpy as np

# 读取文件
img = cv2.imread('E:\\opencv_photo\\hello.jpeg')
print(img.shape)

# 将图片转化为单通道
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY )
print(gray.shape)

#二值化
ret, binary = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY)
# ret为转化是否成功判断，binary为二值化图像，150为阈值
# print(ret)

# 轮廓查找
contours, hierarchy = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# contours 为轮廓    hierarchy为层级
# print(contours)

# 绘制轮廓
# cv2.drawContours(img,contours, 0 ,(255,255,0), 1)

# # 计算面积
# area = cv2.contourArea(contours[0])
# print("area = %d"%(area))

# # 计算周长
# len = cv2.arcLength(contours[0], True)
# # 若为false，则会少计算一条边
# print("len = %d"%(len))
def drawShape(src, points):
    i = 0
    while i < len(points):
        if(i == len(points) - 1):
            x, y = points[i][0]
            x1, y1 = points[0][0]
            cv2.line(src, (x, y), (x1, y1), (0, 0, 255), 3)
        else:
            x, y = points[i][0]
            x1, y1 = points[i+1][0]
            cv2.line(src, (x, y), (x1, y1), (0, 0, 255), 3)
        i = i+1
# 多边形逼近
# e = 20
# approx = cv2.approxPolyDP(contours[0], e, True)

# drawShape(img, approx)

# 凸包
# hull = cv2.convexHull(contours[0])
# drawShape(img, hull)

# 最小外界矩形
r = cv2.minAreaRect(contours[1])
box = cv2.boxPoints(r)  #取出其中的宽和高
box = np.int0(box)
cv2.drawContours(img, [box], 0, (0,0,255),2)    #轮廓绘制

# 最大外界矩形
x, y , w, h = cv2.boundingRect(contours[1])
cv2.rectangle(img, (x,y),(x+w, y+h), (255, 0, 0), 2)


cv2.imshow('img',img)
# #cv2.imshow('gray',gray)
# cv2.imshow('binary',binary)

cv2.waitKey(0)
